knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)Mental health challenges among university students are a growing concern, with academic stress often contributing to anxiety and depression. This analysis examines the impact of exam and non-exam periods on SSRI and SNRI prescription rates across Scottish health boards from 2018 to 2023.
Key questions include: - How do prescription rates differ between exam and non-exam periods? - How do demographic factors, such as the percentage of young adults (17–25), influence trends? - Which health boards show the highest changes in prescription rates?
By integrating prescription, demographic, and geographic data, this study highlights seasonal and regional variations in mental health support needs, offering insights for targeted interventions and resource allocation.
library(janitor)
library(here)
library(dbplyr)
library(tidyverse)Load and clean demographic datasets
HB_names dataset is processed to select only the health
board code and names.population_data is processed to extract the total
population of each health board.age_data is processed to extract the total number of
young adults aged 17 to 25, the typical age range of university
students. The dataset is then joined with the
population_data to create a column of percentage. This
would give a clear view of which health board regions to consider in the
analysis.library(stringr)
# Load and clean Health board names data
HB_names <- read_csv("C:/Data_Science/B223640/data/HB_names.csv") %>%
clean_names() %>%
select(hb, hb_name) %>%
rename(hbt = hb) %>%
mutate(hb_name = str_remove(hb_name, "^NHS\\s"))
# Load and clean population data
population_data <- read_csv("C:/Data_Science/B223640/data/UV103_age_health_board_census.csv", skip = 10) %>%
rename(hb_name = "Health Board Area 2019",
hb_population = Count) %>%
filter(Age == "All people" & Sex == "All people") %>%
select(hb_name, hb_population) %>%
mutate(hb_name = str_remove(hb_name, "^NHS\\s")) %>%
arrange(desc(hb_population))
age_data <- read.csv("C:/Data_Science/B223640/data/UV103_age_health_board_census.csv", skip = 10) %>%
filter(str_detect(Age, "17|18|19|20|21|22|23|24|25")) %>%
rename(hb_name = "Health.Board.Area.2019") %>%
group_by(hb_name) %>%
summarise(totalpeople = sum(Count)) %>%
left_join(population_data, by = "hb_name") %>%
mutate(percentage = totalpeople * 100 / hb_population) %>%
arrange(desc(percentage))Loading the Exam Season and Non-Exam Season Datasets from 2018 to 2023
The exam season includes: - Semester 1 finals (November and December). - Semester 2 finals (April and May), as SSRI and SNRI treatments typically require at least four weeks to take effect (Ankrom, 2024).
The non-exam season is defined as: - The first two months of the university semester: - Semester 1 start: September and October. - Semester 2 start: January and February. - During these periods, students typically experience lighter workloads and less stress compared to exam periods.
library(tidyverse)
# Define column_types first to ensure consistent data parsing across all files by explicitly defining how each column should be interpreted
column_types <- cols(
HBT = col_character(),
DMDCode = col_character(),
BNFItemCode = col_character(),
BNFItemDescription = col_character(),
PrescribedType = col_character(),
GPPractice = col_double(),
NumberOfPaidItems = col_double(),
PaidQuantity = col_double(),
GrossIngredientCost = col_double(),
PaidDateMonth = col_double()
)
# List all CSV files in the "examszn_18-23" folder
examfiles <- list.files("C:/Data_Science/B223640/data/examszn_18-23", pattern = "csv", full.names = TRUE)
examszndata <- examfiles %>%
map_dfr(~read_csv(., col_types = column_types))
# List all CSV files in the "nonexamszn_18-23" folder
nonexamfiles <- list.files("C:/Data_Science/B223640/data/nonexamszn_18-23", pattern = "csv", full.names = TRUE)
nonexamszndata <- nonexamfiles %>%
map_dfr(~read_csv(., col_types = column_types))Steps for Analysis and the Role of
pivot_wider()
pivot_wider() to convert the data from long to wide
format, where each drug becomes a separate column:gt package to generate a well-formatted table that
summarizes key metrics, including per capita rates, demographic data,
and percentage changes, for clear presentation of findings.# Load required libraries
library(tidyverse)
library(gt)
library(dplyr)
# Define a reusable function to process wide-format data
process_wide_data <- function(data) {
data %>%
clean_names() %>%
mutate(bnf_item_description = str_remove(bnf_item_description, "[_\\s].*")) %>%
full_join(HB_names) %>%
full_join(age_data) %>%
filter(str_detect(bnf_item_description, "ESCITALOPRAM|SERTRALINE|FLUOXETINE|VENLAFAXINE|PAROXETINE|CITALOPRAM")) %>%
filter(str_detect(hb_name, "Lothian|Grampian|Greater Glasgow and Clyde|Tayside|Fife|Forth Valley|Lanarkshire")) %>%
mutate(hb_name = str_remove(hb_name, "^NHS\\s")) %>%
select(hb_name, bnf_item_description, paid_date_month, hb_population, paid_quantity) %>%
group_by(hb_name, bnf_item_description) %>%
summarise(total_prescriptions = sum(paid_quantity), .groups = "drop") %>%
pivot_wider(
names_from = bnf_item_description,
values_from = total_prescriptions,
values_fill = list(total_prescriptions = 0)
) %>%
rowwise() %>% # Ensure row-wise summation
mutate(total_prescriptions = sum(c_across(CITALOPRAM:VENLAFAXINE), na.rm = TRUE)) %>%
ungroup() %>%
full_join(age_data) %>%
drop_na() # Remove rows with NA values
}
# Process exam and non-exam data using the function
examszndatawide <- process_wide_data(examszndata)
nonexamszndatawide <- process_wide_data(nonexamszndata)
# Add prescription per capita to examszndatawide and nonexamszndatawide
examszndatawide <- examszndatawide %>%
mutate(prescription_per_capita = total_prescriptions / hb_population)
nonexamszndatawide <- nonexamszndatawide %>%
mutate(prescription_per_capita = total_prescriptions / hb_population)# Combine data for comparison, including the percentage column
combined_prescriptions <- examszndatawide %>%
select(hb_name, prescription_per_capita_exam = prescription_per_capita, percentage) %>%
left_join(
nonexamszndatawide %>%
select(hb_name, prescription_per_capita_non_exam = prescription_per_capita),
by = "hb_name"
)
# Add a change column to show the percentage difference between exam and non-exam seasons
combined_prescriptions <- combined_prescriptions %>%
mutate(
change_percent = ((prescription_per_capita_exam - prescription_per_capita_non_exam) / prescription_per_capita_non_exam) * 100
)
# Create a prettier table with grand_summary_rows
pretty_table <- combined_prescriptions %>%
arrange(desc(prescription_per_capita_exam)) %>%
gt() %>%
tab_header(
title = "Prescription Per Capita and Age Group Percentage",
subtitle = "Exam Season vs Non-Exam Season (2018-2023)"
) %>%
fmt_number(
columns = vars(prescription_per_capita_exam, prescription_per_capita_non_exam, percentage, change_percent),
decimals = 2
) %>%
cols_label(
hb_name = "Health Board",
prescription_per_capita_exam = "Exam Season (Per Capita)",
prescription_per_capita_non_exam = "Non-Exam Season (Per Capita)",
percentage = "17–25 Age Group (%)",
change_percent = "Change (%)"
) %>%
tab_spanner(
label = "Prescription Per Capita",
columns = vars(prescription_per_capita_exam, prescription_per_capita_non_exam, change_percent)
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels(everything())
) %>%
tab_style(
style = cell_fill(color = "lightblue"),
locations = cells_body(
columns = vars(change_percent),
rows = change_percent > 0
)
) %>%
tab_style(
style = cell_fill(color = "lightpink"),
locations = cells_body(
columns = vars(change_percent),
rows = change_percent < 0
)
) %>%
grand_summary_rows(
columns = vars(prescription_per_capita_exam, prescription_per_capita_non_exam, percentage),
fns = list(
Avg = ~mean(., na.rm = TRUE)
),
formatter = fmt_number,
decimals = 2
) %>%
tab_source_note(
source_note = "Data sourced from 2018-2023 Prescription Records"
)
# Print the improved table
pretty_table| Prescription Per Capita and Age Group Percentage | |||||
| Exam Season vs Non-Exam Season (2018-2023) | |||||
| Health Board |
Prescription Per Capita
|
17–25 Age Group (%) | |||
|---|---|---|---|---|---|
| Exam Season (Per Capita) | Non-Exam Season (Per Capita) | Change (%) | |||
| Greater Glasgow and Clyde | 83.41 | 80.25 | 3.93 | 25.06 | |
| Forth Valley | 82.67 | 78.52 | 5.28 | 21.22 | |
| Fife | 80.26 | 74.47 | 7.77 | 21.54 | |
| Lanarkshire | 72.08 | 72.50 | −0.59 | 19.75 | |
| Grampian | 68.26 | 64.58 | 5.70 | 20.83 | |
| Lothian | 64.53 | 61.34 | 5.20 | 25.61 | |
| Tayside | 61.97 | 56.09 | 10.48 | 22.20 | |
| Avg | — | 73.31 | 69.68 | — | 22.32 |
| Data sourced from 2018-2023 Prescription Records | |||||
Key Findings
The table reveals that most health boards show higher prescription rates during exam periods, with an average increase from 69.68 to 73.31 prescriptions per capita. Tayside (+10.48%) and Fife (+7.77%) exhibit the most significant increases, reflecting heightened academic stress. In contrast, Lanarkshire shows a slight decrease (-0.59%), indicating regional variations in mental health needs or prescribing practices.
While regions with higher young adult populations, such as Greater Glasgow and Clyde (25.06%), generally have elevated prescription rates, Lothian (25.61%) does not follow this trend, suggesting alternative support systems or differing prescribing norms.
These findings highlight the need for targeted mental health interventions during exam periods, particularly in regions with significant increases, and further investigation into prescribing disparities to ensure equitable support across Scotland.
Define a Function to Process the Data
The following function processes the exam and non-exam datasets: -
Cleans column names. - Joins with demographic data
(HB_names, population_data, and
age_data). - Filters for SSRIs and SNRIs commonly
prescribed for exam-related stress. - Focuses on specific health boards
with high percentages of young adults. - Calculates total prescriptions
and per-person prescription rates.
# Define a function to process the data
process_data <- function(data, season) {
data %>%
clean_names() %>%
mutate(bnf_item_description = str_remove(bnf_item_description, "[_\\s].*")) %>%
full_join(HB_names) %>%
full_join(population_data) %>%
full_join(age_data) %>%
filter(str_detect(bnf_item_description, "ESCITALOPRAM|SERTRALINE|FLUOXETINE|VENLAFAXINE|PAROXETINE|CITALOPRAM")) %>%
filter(str_detect(hb_name, "Lothian|Grampian|Greater Glasgow and Clyde|Tayside|Fife|Forth Valley|Lanarkshire")) %>%
group_by(hb_name, bnf_item_description, totalpeople, percentage, hb_population) %>%
summarise(total_prescription = sum(paid_quantity, na.rm = TRUE)) %>%
filter(!is.na(bnf_item_description)) %>%
mutate(
Season = season,
per_person = total_prescription / hb_population
)
}
# Process exam and non-exam data
examszndata1 <- process_data(examszndata, "examseason")
nonexamszndata1 <- process_data(nonexamszndata, "nonexamseason")
# Combine the datasets
combined_data <- bind_rows(examszndata1, nonexamszndata1) %>%
filter(!is.na(bnf_item_description))library(forcats)
library(ggplot2)
library(plotly)
# Create the ggplot without text labels
p <- ggplot(combined_data, aes(
x = total_prescription,
y = fct_reorder(bnf_item_description, total_prescription, .fun = sum, .desc = TRUE), # Reorder drugs in descending order
fill = Season
)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
labs(
title = "Comparison of Total Prescriptions by Drug",
x = "Total Prescriptions",
y = "Drug",
fill = "Season"
) +
theme_minimal() +
facet_wrap(~fct_reorder(hb_name, total_prescription, .fun = sum, .desc = TRUE), ncol = 2) # Reorder health boards in descending order
# Make the plot interactive with only `total_prescription` and `Season` in the tooltip
interactive_plot <- ggplotly(p, tooltip = c("x", "fill"))
# Display the interactive plot
interactive_plotResults: Seasonal Variations in SSRI and SNRI Prescriptions Across Scottish Health Boards
Total prescriptions for SSRIs and SNRIs differ between exam and non-exam seasons, with higher rates during exam periods, suggesting increased stress among students.
Greater Glasgow and Clyde and Lothian have the highest total prescriptions, correlating with larger student populations.
Fife, Forth Valley, and Tayside show lower totals but follow similar seasonal patterns.
Sertraline, Fluoxetine, and Citalopram are the most commonly prescribed drugs, while Escitalopram and Paroxetine have lower rates.
Exam seasons show a slight increase in prescriptions, highlighting academic stress as a driver for mental health support.
Combining and Analyzing Prescription Data Across Exam and Non-Exam Periods
This code combines the exam and non-exam datasets
(examszndata and nonexamszndata) into a single
dataset, joinedfulldata. Key steps include: - Cleaning
column names for consistency. - Joining with demographic datasets
(HB_names and population_data) - Filtering the
data to focus on: - Six health boards with a high prevalence of young
adults: Lothian, Grampian,
Greater Glasgow and Clyde, Tayside,
Fife, Forth Valley, and
Lanarkshire. - Six commonly prescribed SSRIs and SNRIs:
Escitalopram, Sertraline,
Fluoxetine, Venlafaxine,
Paroxetine, and Citalopram. -
Extracting year and categorizing data into four academic periods:
Semester 1 Finals, Semester 2 Finals,
Semester 1 Start, and Semester 2
Start. - Grouping by year and period
for analysis.
joinedfulldata <- examszndata %>%
full_join(nonexamszndata)
joinedfulldata <- joinedfulldata %>%
clean_names() %>%
full_join(HB_names) %>%
full_join(population_data) %>%
select(hb_name, bnf_item_description, paid_date_month, paid_quantity) %>%
filter(str_detect(bnf_item_description, "ESCITALOPRAM|SERTRALINE|FLUOXETINE|VENLAFAXINE|PAROXETINE|CITALOPRAM"))%>%
filter(str_detect(hb_name, "Lothian|Grampian|Greater Glasgow and Clyde|Tayside|Fife|Forth Valley|Lanarkshire")) %>%
mutate(bnf_item_description = str_remove(bnf_item_description, "[_\\s].*")) %>%
mutate(
year = substr(paid_date_month, 1, 4),
period = case_when(
substr(paid_date_month, 5, 6) %in% c("04", "05") ~ "Semester 1 Finals",
substr(paid_date_month, 5, 6) %in% c("11", "12") ~ "Semester 2 Finals",
substr(paid_date_month, 5, 6) %in% c("01", "02") ~ "Semester 2 Start",
substr(paid_date_month, 5, 6) %in% c("09", "10") ~ "Semester 1 Start",
TRUE ~ NA_character_
)) %>%
group_by(year, period)Yearly Trends of Drug Prescriptions by academic period
The following code visualizes the yearly trends in total paid quantities of six commonly prescribed SSRIs and SNRIs across four academic periods: Semester 1 Finals, Semester 2 Finals, Semester 1 Start, and Semester 2 Start. The visualization allows comparisons of prescription trends over time for each drug.
###Key Features: - Line Chart: Displays the trends in total paid quantities over the years, grouped by period. - Faceted by Drug: Each drug (e.g., Citalopram, Sertraline) is shown in a separate panel for clarity. - Dynamic Tooltips: The interactive plot provides additional information when hovering over the lines.
library(stringr)
summary_data2 <- joinedfulldata %>%
group_by(year, period, bnf_item_description) %>%
summarise(total_paid_quantity = sum(paid_quantity, na.rm = TRUE), .groups = "drop")
library(ggplot2)
library(dplyr)
library(plotly)
# Create the ggplot2 chart
interactive_plot <- ggplotly(
ggplot(summary_data2, aes(x = year, y = total_paid_quantity, color = period, group = period)) +
geom_line(size = 1) +
geom_point(size = 2) +
facet_wrap(~ bnf_item_description, scales = "free_y", ncol = 2) + # Adjust number of columns for better spacing
labs(
title = "Yearly Trends of Drug Prescriptions",
x = "Year",
y = "Total Paid Quantity",
color = "Academic Period"
) +
theme_minimal() +
theme(
strip.text = element_text(size = 8, face = "bold"), # Reduce facet strip text size
axis.text.x = element_text(angle = 45, hjust = 1, size = 8), # Reduce x-axis label size
axis.text.y = element_text(size = 8) # Reduce y-axis label size
),
tooltip = c("x", "y", "color") # Keep relevant tooltips only
)
interactive_plotThe consistently higher prescription rates during Semester 2 Finals underscore the critical mental health challenges faced by students during this period. This finding highlights the importance of targeted interventions, such as counseling, stress management workshops, and peer support programs, during the second semester to address the heightened needs of the student population.
```